Efficient intensity-based camera pose estimation in presence of depth

Maha El Choubassi; Oscar Nestares; Yi Wu; Igor Kozintsev; Horst Haussecker

doi:10.1117/12.2005729

12 March 2013 Efficient intensity-based camera pose estimation in presence of depth

Maha El Choubassi, Oscar Nestares, Yi Wu, Igor Kozintsev, Horst Haussecker

Proceedings Volume 8650, Three-Dimensional Image Processing (3DIP) and Applications 2013; 86500I (2013) https://doi.org/10.1117/12.2005729
Event: IS&T/SPIE Electronic Imaging, 2013, Burlingame, California, United States

Abstract

The widespread success of Kinect enables users to acquire both image and depth information with satisfying accuracy at relatively low cost. We leverage the Kinect output to efficiently and accurately estimate the camera pose in presence of rotation, translation, or both. The applications of our algorithm are vast ranging from camera tracking, to 3D points clouds registration, and video stabilization. The state-of-the-art approach uses point correspondences for estimating the pose. More explicitly, it extracts point features from images, e.g., SURF or SIFT, and builds their descriptors, and matches features from different images to obtain point correspondences. However, while features-based approaches are widely used, they perform poorly in scenes lacking texture due to scarcity of features or in scenes with repetitive structure due to false correspondences. Our algorithm is intensity-based and requires neither point features’ extraction, nor descriptors’ generation/matching. Due to absence of depth, the intensity-based approach alone cannot handle camera translation. With Kinect capturing both image and depth frames, we extend the intensity-based algorithm to estimate the camera pose in case of both 3D rotation and translation. The results are quite promising.

Citation Download Citation

Maha El Choubassi, Oscar Nestares, Yi Wu, Igor Kozintsev, and Horst Haussecker "Efficient intensity-based camera pose estimation in presence of depth", Proc. SPIE 8650, Three-Dimensional Image Processing (3DIP) and Applications 2013, 86500I (12 March 2013); https://doi.org/10.1117/12.2005729

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KEYWORDS

Cameras

3D image processing

Video

Imaging systems

Stereoscopic cameras

Clouds

Feature extraction

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